@article{WANG2023, 
author = {Yuxuan WANG and Bo JIANG and Cheng LIU and Tao YU and Xiaoxuan CHEN and Yufang YUAN and Lin WANG},
title = {Applied of deep U-Net in remote sensing mountain glacier boundary segmentation},
year = {2023},
journal = {Journal of Northwest University (Natural Science Edition)},
volume = {53},
number = {2},
pages = {256-264},
keywords = {mountain glacier, boundary segmentation, U-Net},
url = {https://www.sciopen.com/article/10.16152/j.cnki.xdxbzr.2023-02-011},
doi = {10.16152/j.cnki.xdxbzr.2023-02-011},
abstract = {Glacier change has always been the key direction of Geographical research and has an important impact on the local climate environment and water resources environment. No matter what direction of research, the first problem is to extract the range of glaciers. With the development of remote sensing technology, extracting the range of glacier from remote sensing images has become the first way of related research. Compared with the artificial visual interpretation method which has long time-consuming, low efficiency and large subjective factors, deep learning has certain advantages. This paper uses the traditional deep learning semantic segmentation network to segment glaciers. However, because of lack of glacier training dataset, the traditional u-net semantic segmentation network has great interference in the segmentation of true color images in glacier areas, which cannot highlight the characteristics of glaciers and has low efficiency in glacier segmentation. Therefore, through the vector data of glaciers and based on Landsat 8 remote sensing satellite images, we establish glacier segmentation training dataset, which can make full use of the advantages of remote sensing multi band images and strengthen the glacier feature information. At the same time, by adding additional color images combined with different bands, it enriches the glacier segmentation information. And it will improve the accuracy of glacier segmentation by using two feature information in deep learning module. The experimental results show that the proposed method can segment the glacier range, which is better than other depth learning methods.}
}